| Description |
1 online resource (xxxii, 336 pages) : illustrations |
| Series |
River Publishers series in signal, image and speech processing |
|
River Publishers series in signal, image and speech processing.
|
| Contents |
Markov Chain and its Applications -- Hidden Markov Modelling (HMM) -- Introduction to Kalman Filters -- Kalman Filters II -- Genetic Algorithm -- Calculus on Computational Graphs -- Support Vector Machines -- Artificial Neural Networks -- Training of Neural Networks -- Recurrent Neural Networks -- Convolutional Neural Networks -- Principal Component Analysis -- Moment-Generating Functions -- Characteristic Functions -- Probability-Generating Functions -- Digital Identity Management System Using Neural Networks -- Probabilistic Neural Networks Classifiers for IoT Data Classification -- MML Learning and Inference of Hierarchical Probabilistic Finite State Machines |
| Summary |
This text provides some of the most sought after techniques in big data analytics. Establishing strong foundations in these topics provides practical ease when big data analyses are undertaken using the widely available open source and commercially orientated computation platforms, languages and visualization systems. The book, when combined with such platforms, provides a complete set of tools required to handle big data and can lead to fast implementations and applications. The book contains a mixture of machine learning foundations, deep learning, artificial intelligence, statistics and evolutionary learning mathematics written from the usage point of view with rich explanations on what the concepts mean |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Online resource; title from PDF title page (EBSCOhost, viewed April 12, 2021) |
| Subject |
Big data.
|
|
Data mining.
|
|
Quantitative research.
|
|
Data Mining
|
|
Big data
|
|
Data mining
|
|
Quantitative research
|
| Form |
Electronic book
|
| ISBN |
9788770220958 |
|
8770220956 |
|
